SoroushVahidi/scidocs huggingface dataset:
Deep CNNs With Spatially Weighted Pooling for Fine-Grained Car Recognition
(2017)

Fine-grained car recognition aims to recognize the category information of a car, such as car make, car model, or even the year of manufacture. A number of recent studies have shown that a deep convolutional neural network (DCNN) trained on a large-scale data set can achieve impressive results at a range of generic object classification tasks. In this paper, we propose a spatially weighted pooling (SWP) strategy, which considerably improves the robustness and effectiveness of the feature representation of most dominant DCNNs. More specifically, the SWP is a novel pooling layer, which contains a predefined number of spatially weighted masks or pooling channels. The SWP pools the extracted features of DCNNs with the guidance of its learnt masks, which measures the importance of the spatial units in terms of discriminative power. As the existing methods that apply uniform grid pooling on the convolutional feature maps of DCNNs, the proposed method can extract the convolutional features and generate the pooling channels from a single DCNN. Thus minimal modification is needed in terms of implementation. Moreover, the parameters of the SWP layer can be learned in the end-to-end training process of the DCNN. By applying our method to several fine-grained car recognition data sets, we demonstrate that the proposed method can achieve better performance than recent approaches in the literature. We advance the state-of-the-art results by improving the accuracy from 92.6% to 93.1% on the Stanford Cars-196 data set and 91.2% to 97.6% on the recent CompCars data set. We have also tested the proposed method on two additional large-scale data sets with impressive results observed.
The Art of Multiple Sequence Alignment in R
(2016)

Figure 1: The art of multiple sequence alignment. This document is intended to illustrate the art of multiple sequence alignment in R using DECIPHER. Even though its beauty is often concealed, multiple sequence alignment is a form of art in more ways than one. Take a look at Figure 1 for an illustration of what is happening behind the scenes during multiple sequence alignment. The practice of sequence alignment is one that requires a degree of skill, and it is that art which this vignette intends to convey. It is simply not enough to “plug” sequences into a multiple sequence aligner and blindly trust the result. An appreciation for the art as well a careful consideration of the results are required. What really is multiple sequence alignment, and is there a single correct alignment? Generally speaking, alignment seeks to perform the act of taking multiple divergent biological sequences of the same “type” and fitting them to a form that reflects some shared quality. That quality
Incorporating query-specific feedback into learning-to-rank models
(2014)

Relevance feedback has been shown to improve retrieval for a broad range of retrieval models. It is the most common way of adapting a retrieval model for a specific query. In this work, we expand this common way by focusing on an approach that enables us to do query-specific modification of a retrieval model for learning-to-rank problems. Our approach is based on using feedback documents in two ways: 1) to improve the retrieval model directly and 2) to identify a subset of training queries that are more predictive than others. Experiments with the Gov2 collection show that this approach can obtain statistically significant improvements over two baselines; learning-to-rank (SVM-rank) with no feedback and learning-to-rank with standard relevance feedback.
EuroSense: Automatic Harvesting of Multilingual Sense Annotations from Parallel Text
(2017)

Parallel corpora are widely used in a variety of Natural Language Processing tasks, from Machine Translation to cross-lingual Word Sense Disambiguation, where parallel sentences can be exploited to automatically generate high-quality sense annotations on a large scale. In this paper we present EUROSENSE, a multilingual sense-annotated resource based on the joint disambiguation of the Europarl parallel corpus, with almost 123 million sense annotations for over 155 thousand distinct concepts and entities from a languageindependent unified sense inventory. We evaluate the quality of our sense annotations intrinsically and extrinsically, showing their effectiveness as training data for Word Sense Disambiguation.
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
(2018)

One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth. This paper demystifies this surprising phenomenon for two-layer fully connected ReLU activated neural networks. For anm hidden node shallow neural network with ReLU activation and n training data, we show as long asm is large enough and no two inputs are parallel, randomly initialized gradient descent converges to a globally optimal solution at a linear convergence rate for the quadratic loss function. Our analysis relies on the following observation: over-parameterization and random initialization jointly restrict every weight vector to be close to its initialization for all iterations, which allows us to exploit a strong convexity-like property to show that gradient descent converges at a global linear rate to the global optimum. We believe these insights are also useful in analyzing deep models and other first order methods.
Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set
(2012)

We address a cross-scenario clothing retrieval problem- given a daily human photo captured in general environment, e.g., on street, finding similar clothing in online shops, where the photos are captured more professionally and with clean background. There are large discrepancies between daily photo scenario and online shopping scenario. We first propose to alleviate the human pose discrepancy by locating 30 human parts detected by a well trained human detector. Then, founded on part features, we propose a two-step calculation to obtain more reliable one-to-many similarities between the query daily photo and online shopping photos: 1) the within-scenario one-to-many similarities between a query daily photo and an extra auxiliary set are derived by direct sparse reconstruction; 2) by a cross-scenario many-to-many similarity transfer matrix inferred offline from the auxiliary set and the online shopping set, the reliable cross-scenario one-to-many similarities between the query daily photo and all online shopping photos are obtained.
Robust Subspace Segmentation by Low-Rank Representation
(2010)

We propose low-rank representation (LRR) to segment data drawn from a union of multiple linear (or affine) subspaces. Given a set of data vectors, LRR seeks the lowestrank representation among all the candidates that represent all vectors as the linear combination of the bases in a dictionary. Unlike the well-known sparse representation (SR), which computes the sparsest representation of each data vector individually, LRR aims at finding the lowest-rank representation of a collection of vectors jointly. LRR better captures the global structure of data, giving a more effective tool for robust subspace segmentation from corrupted data. Both theoretical and experimental results show that LRR is a promising tool for subspace segmentation.
Reducing the energy cost of human walking using an unpowered exoskeleton
(2015)

With efficiencies derived from evolution, growth and learning, humans are very well-tuned for locomotion. Metabolic energy used during walking can be partly replaced by power input from an exoskeleton, but is it possible to reduce metabolic rate without providing an additional energy source? This would require an improvement in the efficiency of the human–machine system as a whole, and would be remarkable given the apparent optimality of human gait. Here we show that the metabolic rate of human walking can be reduced by an unpowered ankle exoskeleton. We built a lightweight elastic device that acts in parallel with the user's calf muscles, off-loading muscle force and thereby reducing the metabolic energy consumed in contractions. The device uses a mechanical clutch to hold a spring as it is stretched and relaxed by ankle movements when the foot is on the ground, helping to fulfil one function of the calf muscles and Achilles tendon. Unlike muscles, however, the clutch sustains force passively. The exoskeleton consumes no chemical or electrical energy and delivers no net positive mechanical work, yet reduces the metabolic cost of walking by 7.2 ± 2.6% for healthy human users under natural conditions, comparable to savings with powered devices. Improving upon walking economy in this way is analogous to altering the structure of the body such that it is more energy-effective at walking. While strong natural pressures have already shaped human locomotion, improvements in efficiency are still possible. Much remains to be learned about this seemingly simple behaviour.
A spelling correction model for end-to-end speech recognition
(2019)

Attention-based sequence-to-sequence models for speech recognition jointly train an acoustic model, language model (LM), and alignment mechanism using a single neural network and require only parallel audio-text pairs. Thus, the language model component of the end-to-end model is only trained on transcribed audio-text pairs, which leads to performance degradation especially on rare words. While there have been a variety of work that look at incorporating an external LM trained on text-only data into the end-to-end framework, none of them have taken into account the characteristic error distribution made by the model. In this paper, we propose a novel approach to utilizing text-only data, by training a spelling correction (SC) model to explicitly correct those errors. On the LibriSpeech dataset, we demonstrate that the proposed model results in an 18.6% relative improvement in WER over the baseline model when directly correcting top ASR hypothesis, and a 29.0% relative improvement when further rescoring an expanded n-best list using an external LM.
Yield optimization using advanced statistical correlation methods
(2014)

This work presents a novel yield optimization methodology based on establishing a strong correlation between a group of fails and an adjustable process parameter. The core of the methodology comprises three advanced statistical correlation methods. The first method performs multivariate correlation analysis to uncover linear correlation relationships between groups of fails and measurements of a process parameter. The second method partitions a dataset into multiple subsets and tries to maximize the average of the correlations each calculated based on one subset. The third method performs statistical independence test to evaluate the risk of adjusting a process parameter. The methodology was applied to an automotive product line to improve yield. Five process parameter changes were discovered which led to significant improvement of the yield and consequently significant reduction of the yield fluctuation.
Development trends for human monoclonal antibody therapeutics
(2010)

Fully human monoclonal antibodies (mAbs) are a promising and rapidly growing category of targeted therapeutic agents. The first such agents were developed during the 1980s, but none achieved clinical or commercial success. Advances in technology to generate the molecules for study — in particular, transgenic mice and yeast or phage display — renewed interest in the development of human mAbs during the 1990s. In 2002, adalimumab became the first human mAb to be approved by the US Food and Drug Administration (FDA). Since then, an additional six human mAbs have received FDA approval: panitumumab, golimumab, canakinumab, ustekinumab, ofatumumab and denosumab. In addition, 3 candidates (raxibacumab, belimumab and ipilimumab) are currently under review by the FDA, 7 are in Phase III studies and 81 are in either Phase I or II studies. Here, we analyse data on 147 human mAbs that have entered clinical study to highlight trends in their development and approval, which may help inform future studies of this class of therapeutic agents.
Changes in structural and functional connectivity among resting-state networks across the human lifespan
(2014)

At rest, the brain's sensorimotor and higher cognitive systems engage in organized patterns of correlated activity forming resting-state networks. An important empirical question is how functional connectivity and structural connectivity within and between resting-state networks change with age. In this study we use network modeling techniques to identify significant changes in network organization across the human lifespan. The results of this study demonstrate that whole-brain functional and structural connectivity both exhibit reorganization with age. On average, functional connections within resting-state networks weaken in magnitude while connections between resting-state networks tend to increase. These changes can be localized to a small subset of functional connections that exhibit systematic changes across the lifespan. Collectively, changes in functional connectivity are also manifest at a system-wide level, as components of the control, default mode, saliency/ventral attention, dorsal attention, and visual networks become less functionally cohesive, as evidenced by decreased component modularity. Paralleling this functional reorganization is a decrease in the density and weight of anatomical white-matter connections. Hub regions are particularly affected by these changes, and the capacity of those regions to communicate with other regions exhibits a lifelong pattern of decline. Finally, the relationship between functional connectivity and structural connectivity also appears to change with age; functional connectivity along multi-step structural paths tends to be stronger in older subjects than in younger subjects. Overall, our analysis points to age-related changes in inter-regional communication unfolding within and between resting-state networks.
A Nested-Graph Model for the Representation and Manipulation of Complex Objects
(1994)

Three recent trends in database research are object-oriented and deductive databases and graph-based user interfaces. We draw these trends together in a data model we call the Hypernode Model. The single data structure of this model is the hypernode, a graph whose nodes can themselves be graphs. Hypernodes are typed, and types, too, are nested graphs. We give the theoretical foundations of hypernodes and types, and we show that type checking is tractable. We show also how conventional type-forming operators can be simulated by our graph types, including cyclic types. The Hypernode Model comes equipped with a rule-based query language called Hyperlog, which is complete with respect to computation and update. We define the operational semantics of Hyperlog and show that the evaluation can be performed efficiently. We discuss also the use of Hyperlog for supporting database browsing, an essential feature of Hypertext databases. We compare our work with other graph-based data models—unlike previous graph-based models, the Hypernode Model provides inherent support for data abstraction via its nesting of graphs. Finally, we briefly discuss the implementation of a DBMS based on the Hypernode Model.
Fast Compact City Modeling for Navigation Pre-Visualization
(2006)

Nowadays, GPS-based car navigation systems mainly use speech and aerial views of simplified road maps to guide drivers to their destination. However, drivers often experience difficulties in linking the simple 2D aerial map with the visual impression that they get from the real environment, which is inherently ground-level based. Therefore, supplying realistically textured 3D city models at ground-level proves very useful for pre-visualizing an upcoming traffic situation. Because this pre-visualization can be rendered from the expected future viewpoints of the driver, the latter will more easily understand the required maneuver. 3D city models can be reconstructed from the imagery recorded by surveying vehicles. The vastness of image material gathered by these vehicles, however, puts extreme demands on vision algorithms to ensure their practical usability. Algorithms need to be as fast as possible and should result in compact, memory efficient 3D city models for future ease of distribution and visualization. For the considered application, these are not contradictory demands. Simplified geometry assumptions can speed up vision algorithms while automatically guaranteeing compact geometry models. We present a novel city modeling framework which builds upon this philosophy to create 3D content at high speed which could allow for pre-visualization of any conceivable traffic situation by car navigation modules.
Design of impedance matching circuits for RF energy harvesting systems
(2017)

This paper presents a systematic design methodology for impedance matching circuits of an RF energy harvester to maximize the harvested energy for a range of input power levels with known distribution. Design of input matching networks for RF rectifier differs from those for traditional RF circuits such as low-noise amplifier because the transistors in the RF rectifiers operate in different regions. In this work, it is shown that the input impedance of the rectifier estimated based on small signal simulation of transistors at the peak of input signal in the rectifier when the transistors are biased with stable DC operating voltage provides the largest harvested energy from a fixed input power. For variable input power levels, a matching network selection strategy is proposed to maximize expected harvested energy given the probability density distribution of input power levels. As an experimental sample, an RF energy harvester circuit is designed to maximize the harvested energy in the 902–928 MHz band employing an off-chip impedance matching circuit designed using the proposed methodology. The proposed RF energy harvester exhibits a measured maximum power conversion efficiency (PCE) of 32% at −15 dBm (32 μW) and delivers an output DC voltage of 3.2 V to a 1 MΩ load.
Fast and Accurate Calibration of a Kinect Sensor
(2013)

The article describes a new algorithm for calibrating a Kinect sensor that achieves high accuracy using only 6 to 10 image-disparity pairs of a planar checkerboard pattern. The method estimates the projection parameters for both color and depth cameras, the relative pose between them, and the function that converts kinect disparity units (kdu) into metric depth. We build on the recent work of Herrera et. al [8] that uses a large number of input frames and multiple iterative minimization steps for obtaining very accurate calibration results. We propose several modifications to this estimation pipeline that dramatically improve stability, usability, and runtime. The modifications consist in: (i) initializing the relative pose using a new minimal, optimal solution for registering 3D planes across different reference frames, (ii) including a metric constraint during the iterative refinement to avoid a drift in the disparity to depth conversion, and (iii) estimating the parameters of the depth distortion model in an open-loop post-processing step. Comparative experiments show that our pipeline can achieve a calibration accuracy similar to [8] while using less than 1/6 of the input frames and running in 1/30 of the time.
Ring oscillators for CMOS process tuning and variability control
(2006)

Test structures utilizing ring oscillators to monitor MOSFET ac characteristics for digital CMOS circuit applications are described. The measurements provide information on the average behavior of sets of a few hundred MOSFETs under high speed switching conditions. The design of the ring oscillators is specifically tailored for process centering and monitoring of variability in circuit performance in the manufacturing line as well as in the product. The delay sensitivity to key MOSFET parameter variations in a variety of ring oscillator designs is studied using a compact model for partially depleted silicon on insulator(PD-SOI) technology, but the analysis is equally valid for conventional bulk Si technology. Examples of hardware data illustrating the use of this methodology are taken primarily from experimental hardware in the 90-nm CMOS technology node in PD-SOI. The design and data analysis techniques described here allow very rapid investigation of the sources of variations in circuit delays.
The Power of Word Clusters for Text Classification
(2006)

The recently introducedInformation Bottleneck method [21] provides an information theoretic framework, for extr acting features of one variable, that are relevant for the values of an ther variable. Several previous works already suggeste d applying this method for document clustering, gene expression data a nalysis, spectral analysis and more. In this work we present a novel implementation of this method for supervised text cla ssification. Specifically, we apply the information bottlen eck method to find word-clusters that preserve the information a bout document categories and use these clusters as features for classification. Previous work [1] used a similar clustering procedure to show that word-clusters can significantly redu c the feature space dimensionality, with only a minor change in cl assification accuracy. In this work we present similar resul ts and go further to show that when the training sample is small word clusters can yield significant improvement in classificatio n accuracy (up to18%) over the performance using the words directly.
PSSE: An Architecture For A Personalized Semantic Search Engine
(2010)

Semantic technologies promise a next generation of semantic search engines. General search engines don’t take into consideration the semantic relationships between query terms and other concepts that might be significant to user. Thus, semantic web vision and its core ontologies are used to overcome this defect. The order in which these results are ranked is also substantial. Moreover, user preferences and interests must be taken into consideration so as to provide user a set of personalized results. In this paper we propose, an architecture for a Personalized Semantic Search Engine (PSSE). PSSE is a crawler-based search engine that makes use of multi-crawlers to collect resources from both semantic as well as traditional web resources. In order for the system to reduce processing time, web pages' graph is clustered, then clusters are annotated using document annotation agents that work in parallel. Annotation agents use methods of ontology matching to find resources of the semantic web as well as means of information extraction techniques in order to provide a well description of HTML documents. System ranks resources based on a final score that's calculated based on traditional link analysis, content analysis and a weighted user profile for more personalized results. We have a belief that the merge of these techniques together enhances search results.